Flow-Based Network Analysis of the Caenorhabditis elegans Connectome

Bacik, Karol A.; Beguerisse-Díaz, Mariano; Billeh, Yazan N.; Barahona, Mauricio

doi:10.1371/journal.pcbi.1005055

Cited by 47 publications

(49 citation statements)

References 70 publications

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“…The computational analysis of the structure of ADK (closed conformation, PDB ID:2RGX) using Markov Stability (MS) [2,17,18,19,13,14,20] is presented in Fig. 1.…”

Section: Unsupervised Identification Of Biologically Relevant Graph Pmentioning

confidence: 99%

“…To achieve this, MS exploits the time evolution of a diffusive process on the protein graph to identify groups of atoms that behave similarly over a particular time scale in response to perturbation inputs. We identify relevant partitions as being both highly reproducible (i.e., signalled by dips in the average Variation of Information (VI) [22] of the ensemble of solutions) and temporally persistent (i.e., signalled by long plateaux in time) [13,19] (see Methods).…”

Section: Unsupervised Identification Of Biologically Relevant Graph Pmentioning

confidence: 99%

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Unsupervised Graph-Based Learning Predicts Mutations That Alter Protein Dynamics

Peach

Saman

Yaliraki

et al. 2019

Preprint

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Proteins exhibit complex dynamics across a vast range of time and length scales, from the atomistic to the conformational. Adenylate kinase (ADK) showcases the biological relevance of such inherently coupled dynamics across scales: single mutations can affect large-scale protein motions and enzymatic activity. Here we present a combined computational and experimental study of multiscale structure and dynamics in proteins, using ADK as our system of choice. We show how a computationally efficient method for unsupervised graph partitioning can be applied to atomistic graphs derived from protein structures to reveal intrinsic, biochemically relevant substructures at all scales, without re-parameterisation or a priori coarse-graining. We subsequently perform full alanine and arginine in silico mutagenesis scans of the protein, and score all mutations according to the disruption they induce on the large-scale organisation. We use our calculations to guide Förster Resonance Energy Transfer (FRET) experiments on ADK, and show that mutating residue D152 to alanine or residue V164 to arginine induce a large dynamical shift of the protein structure towards a closed state, in accordance with our predictions. Our computations also predict a graded effect of different mutations at the D152 site as a result of increased coherence between the core and binding domains, an effect confirmed quantitatively through a high correlation (R 2 = 0.93) with the FRET ratio between closed and open populations measured on six mutants.

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“…The computational analysis of the structure of ADK (closed conformation, PDB ID:2RGX) using Markov Stability (MS) [2,17,18,19,13,14,20] is presented in Fig. 1.…”

Section: Unsupervised Identification Of Biologically Relevant Graph Pmentioning

confidence: 99%

Section: Unsupervised Identification Of Biologically Relevant Graph Pmentioning

confidence: 99%

Unsupervised Graph-Based Learning Predicts Mutations That Alter Protein Dynamics

Peach

Saman

Yaliraki

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The definition of the MFGs as directed graphs opens up the application of network-theoretic tools for detecting modules of reaction nodes and the hierarchical relationships among them. In contrast with methods for undirected graphs, the Markov Stability framework [50,55] can be used to detect multi-resolution community structure in directed graphs (Sec. A 2), thus allowing the exploration of the multiscale organisation of metabolic reaction networks.…”

Section: Multiscale Organisation Of Metabolic Flux Graphsmentioning

confidence: 99%

Flux-dependent graphs for metabolic networks

Beguerisse-Díaz

Chacón

Oyarzún

et al. 2018

Preprint

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Cells adapt their metabolic fluxes in response to changes in the environment. We present a framework for the systematic construction of flux-based graphs derived from organism-wide metabolic networks. Our graphs encode the directionality of metabolic fluxes via edges that represent the flow of metabolites from source to target reactions. The methodology can be applied in the absence of a specific biological context by modelling fluxes probabilistically, or can be tailored to different environmental conditions by incorporating flux distributions computed through constraint-based approaches such as Flux Balance Analysis. We illustrate our approach on the central carbon metabolism of Escherichia coli and on a metabolic model of human hepatocytes. The flux-dependent graphs under various environmental conditions and genetic perturbations exhibit systemic changes in their topological and community structure, which capture the re-routing of metabolic fluxes and the varying importance of specific reactions and pathways. By integrating constraint-based models and tools from network science, our framework allows the study of context-specific metabolic responses at a system level beyond standard pathway descriptions.

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“…Extensions to directed graphs. In many cases of interest, the pairwise relationships between samples are asymmetric, e.g., following vs. being followed on a social network [3], or the highly directed synaptic connectivity between neurons [2]. Such asymmetry can be highly informative of the structure of the dataset.…”

mentioning

confidence: 99%

Semi-supervised classification on graphs using explicit diffusion dynamics

Peach¹,

Arnaudon²,

Barahona³

2020

Foundations of Data Science

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Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples. Intuitively, the graph acts as a conduit to channel and bias the inference of class labels. Here, we study classification methods that consider the graph as the originator of an explicit graph diffusion. We show that appending graph diffusion to feature-based learning as an a posteriori refinement achieves state-of-the-art classification accuracy. This method, which we call Graph Diffusion Reclassification (GDR), uses overshooting events of a diffusive graph dynamics to reclassify individual nodes. The method uses intrinsic measures of node influence, which are distinct for each node, and allows the evaluation of the relationship and importance of features and graph for classification. We also present diff-GCN, a simple extension of Graph Convolutional Neural Network (GCN) architectures that leverages explicit diffusion dynamics, and allows the natural use of directed graphs. To showcase our methods, we use benchmark datasets of documents with associated citation data.

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Flow-Based Network Analysis of the Caenorhabditis elegans Connectome

Cited by 47 publications

References 70 publications

Unsupervised Graph-Based Learning Predicts Mutations That Alter Protein Dynamics

Unsupervised Graph-Based Learning Predicts Mutations That Alter Protein Dynamics

Flux-dependent graphs for metabolic networks

Semi-supervised classification on graphs using explicit diffusion dynamics

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